Vehicle location is used in a number of different systems for various purposes. In one common application, vehicular location is used in order to generate directions for an operator of the vehicle in order to navigate to a desired location. Generation of turn-by-turn directions in this type of application only requires a general location of the vehicle, the identified destination, and a symbolic map of roads which are available between the vehicle location and the destination.
Vehicle navigation systems commonly utilize a global positioning system (GPS) receiver which acquires one or more signals from one or more of a number of GPS satellites in order to accurately calculate the receiver position. The GPS receiver acquires and tracks signals consisting of carrier, pseudo random codes and modulated data from various satellites. The receiver correlates locally-generated codes with the codes received from the respective satellites to derive timing information relating the receiver time relative to the local generation of code chips to the satellite time at the times of the transmission of the corresponding code chips. The timing relationship between the receiver time and the transmission times of the various signals at the various satellites can be used in conjunction with the modulated data from the various satellites to generate a position of the receiver with respect to a reference frame shared with the satellites, for example, the earth centered earth fixed (ECEF) frame.
A basic GPS receiver can typically identify the location of the receiver within about 5 meters, which is sufficient for basic navigation applications which provide only turn-by-turn instructions. In some more precise navigation systems, the navigation system informs the driver of the actual lane in which the vehicle must be located in order to make an upcoming turn. A vehicular location accuracy of only 5 meters is inadequate for ascertaining whether or not a vehicle is in the appropriate lane for an upcoming turn.
Vehicle location data has also been incorporated into applications which provide collision avoidance. In some vehicle collision avoidance systems, the system determines the location of both the host vehicle and other vehicles within particular traffic lanes and determines collision probabilities based upon the lane-precise location of the vehicles. In this type of system, vehicular localization with an error of up to 5 meters is inadequate since a traffic lane typically has a width of less than about 4 meters.
In order to provide the more accurate vehicular location data needed both in vehicle collision avoidance systems as well as in more precise navigation systems, various alternatives have been investigated. One such alternative is referred to as a differential GPS system. In a differential GPS system, a group of receivers in an area are used to resolve the inaccuracies in the signal transmitted by the GPS satellites. A set of differential corrections for receivers in the particular area are then developed and used by the GPS receivers to correct their position solutions. Generally, a single differential correction factor will account for most errors in the GPS system, including receiver and/or satellite clock errors, variations in the positions of the satellite(s), and ionospheric and atmospheric delays. Vehicle location estimates using a differential GPS system can reduce errors to on the order of 2 meters. In multi-path environments (typical of dense urban areas), however, errors can be on the order of 50 meters.
Another alternative to the basic GPS system is map based localization. This approach allows localization of a vehicle relative to an image based map as discussed by Levinson, et al., “Map-Based Precision Vehicle Localization in Urban Environments,” Robotics: Science and Systems Conference, 2007. This type of system uses a high resolution image of the road surface and allows for localization accuracies on the order of 10-25 cm. The sensors required by this type of system are scanning Light Detection and Ranging (LIDAR) sensors mounted on the roof the vehicle. This approach requires a vast amount of memory to store the required pre-recorded images and thus cannot be considered to be feasible for current vehicle systems.
Vision based localization systems have also been investigated. Vision based localization systems allow localization of a vehicle relative to a database of images mapped in a global coordinate frame. These systems use cameras as the primary sensor as reported by Konolige, et al., “View-Based Maps,” International Conference on Robotics and Systems 2009, and Cummins et al., “Probabilistic appearance based navigation and loop closing,” International Conference on Robotics and Automation 2007. Localization accuracies for these systems in large urban environments have not been reported.
In yet another approach, vision based lane detection systems which allow detection of the position of the vehicle relative to its current lane have also been developed. These systems are state-of-the-art in typical driver assistance systems. In this type of system, the position and course of the lane marking in front of the vehicle are detected. This information is used to warn the driver before the vehicle leaves the current lane (lane departure warning) or to keep the vehicle in the lane (lane keeping support). These systems typically only provide the vehicle position relative to the current lane.
What is needed therefor is a system that provides lane-precise localization on road ways. A system that does not require a high resolution image of the road, such as required by map based localization, or a database of images such as required by vision based localization, would be beneficial. A system which provides lane-precise localization while using only a symbolic map of roads and lanes would be further beneficial. Another benefit would be a system which exhibits accuracy and reliability even in dense urban environments and changing lighting conditions.